Konstantinos Vamvourellis
London School of Economics and Political Science

Marianne Corvellec
Institute for Globally Distributed Open Research and Education (IGDORE)

Video: https://youtu.be/piQvcVala9I

Abstract

This work began when the two authors met at a software development meeting. Konstantinos was building Bayesian models in his research and wanted to learn how to better manage his research process. Marianne was working on data analysis workflows in industry and wanted to learn more about Bayesian statistics. In this paper, the authors present a Bayesian scientific research workflow for statistical analysis. Drawing on a case study in clinical trials, they demonstrate lessons that other scientists, not necessarily Bayesian, could find useful in their own work. Notably, they can be used to improve productivity and reproducibility in any computational research project.

Keywords

Bayesian statistics, life sciences, clinical trials, probabilistic programming, Stan, PyStan

DOI

10.25080/Majora-4af1f417-014

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Copyright © 2018 Konstantinos Vamvourellis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.